In this talk, we introduce a new adaptive Type-I progressive hybrid censoring scheme which has some advantages over the hybrid censoring schemes already discussed in the literature. We then employ Bayesian decision theory to study the variable sampling plans for the exponential distribution under four different progressive hybrid censoring schemes—Type-I and Type-II progressive hybrid censoring schemes, and adaptive Type-I and Type-II progressive hybrid censoring schemes (see Kundu and Joarder 2006; Childs et al. 2008; Ng et al. 2009; Lin and Huang 2011). The explicit expressions of the Bayes risk of a sampling plan under the selected progressive hybrid censoring schemes are established when a general quadratic loss function, which includes the sampling cost, the time-consuming cost, and the salvage, is used. Finally, numerical comparisons between the proposed optimal sampling plans are made, and the examination of the robustness is performed.

Biography: Chien-Tai Lin is a Professor in the Department of Mathematics, Tamkang University, New Taipei City, Taiwan. She received her Ph.D. in 1993 from Florida State University, Tallahassee, U.S.A. She has been at Tamkang University since 1993. Her research interests include reliability and applications of linear combinations of spacings.